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        <title>The Training Pipes Blog</title>
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        <description>Product updates and guides on S3-compatible storage, NFS and SMB access, and AI data pipelines from Training Pipes.</description>
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        <item>
            <title><![CDATA[Mounting S3 as NFS: Why FUSE Isn't Enough for Production]]></title>
            <link>https://localhost:10000/blog/mount-s3-as-nfs-fuse-limitations</link>
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            <pubDate>Fri, 08 May 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[Searching for 'mount S3 as NFS' turns up a dozen FUSE-based tools. Here's why none of them survive production ML workloads, and what actually works.]]></content:encoded>
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            <title><![CDATA[Stop Using s3fs in Production: Better Alternatives for ML Teams]]></title>
            <link>https://localhost:10000/blog/s3fs-alternatives-production-ml</link>
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            <pubDate>Thu, 30 Apr 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[s3fs-fuse is a fine prototype tool and a dangerous production dependency. Here's what breaks, why, and what to use instead for real ML training workloads.]]></content:encoded>
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            <title><![CDATA[NFS vs S3 for AI Training: When to Use Each]]></title>
            <link>https://localhost:10000/blog/nfs-vs-s3-for-ai-training</link>
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            <pubDate>Thu, 23 Apr 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[NFS and S3 solve different problems — but AI teams have to use both. Here's a clear framework for when each protocol wins, and how to stop choosing between them.]]></content:encoded>
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            <title><![CDATA[Why Object Storage Isn't Enough for Modern ML Training]]></title>
            <link>https://localhost:10000/blog/object-storage-not-enough-for-ml-training</link>
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            <pubDate>Sun, 19 Apr 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[Object storage like S3 is cheap, durable, and infinite — but it isn't a filesystem. Here's why ML teams keep hitting the same wall, and what to do about it.]]></content:encoded>
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            <title><![CDATA[Introducing Training Pipes: S3-Compatible Storage with NFS & SMB]]></title>
            <link>https://localhost:10000/blog/introducing-training-pipes</link>
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            <pubDate>Sat, 18 Apr 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[We're launching Training Pipes — the fastest way to mount your cloud object storage over NFS or SMB, with full S3-compatible APIs. Built for AI teams who need high-throughput data access without managing infrastructure.]]></content:encoded>
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            <title><![CDATA[Bring Your Own S3 Bucket: Unifying AI Storage Across Clouds]]></title>
            <link>https://localhost:10000/blog/bring-your-own-s3-bucket-ai-storage</link>
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            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[You already have data in S3, GCS, R2, or Wasabi. Here's how to bring existing cloud storage into a unified AI-ready storage layer without migration, and why you'd want to.]]></content:encoded>
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            <title><![CDATA[SMB vs NFS for Enterprise AI Teams: Which Protocol Wins?]]></title>
            <link>https://localhost:10000/blog/smb-vs-nfs-enterprise-ai</link>
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            <pubDate>Tue, 09 Jun 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[NFS dominates in Linux-first ML shops; SMB dominates in mixed Windows environments. Here's how to choose, and why enterprise AI teams often end up wanting both.]]></content:encoded>
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            <title><![CDATA[Kubernetes Persistent Volumes for ML: A Storage Pattern Guide]]></title>
            <link>https://localhost:10000/blog/kubernetes-pvc-for-ml-workloads</link>
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            <pubDate>Fri, 05 Jun 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[EBS, EFS, FSx, object storage, CSI drivers — Kubernetes gives you many options for ML storage and all the wrong defaults. Here's the pattern that actually works for training workloads.]]></content:encoded>
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            <title><![CDATA[Sharing Datasets Across Training Runs Without Copying Terabytes]]></title>
            <link>https://localhost:10000/blog/dataset-sharing-across-training-runs</link>
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            <pubDate>Mon, 01 Jun 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[When five engineers each copy the same 20TB dataset into ephemeral storage, you've got a problem. Here's how to share datasets efficiently across teams and runs.]]></content:encoded>
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            <title><![CDATA[The Hidden Cost of Cross-Region Data Egress in ML Pipelines]]></title>
            <link>https://localhost:10000/blog/cross-region-egress-cost-ml-pipelines</link>
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            <pubDate>Thu, 28 May 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[You don't notice egress until you see the bill. Here's how ML training pipelines quietly rack up cross-region transfer costs, and the architecture that fixes it.]]></content:encoded>
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            <title><![CDATA[Checkpointing Large Models: A Storage Guide for ML Engineers]]></title>
            <link>https://localhost:10000/blog/checkpointing-large-models-storage</link>
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            <pubDate>Sun, 24 May 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[Writing a 500GB checkpoint every hour stresses your storage in ways that training data doesn't. Here's how to design a checkpoint pipeline that's fast, reliable, and doesn't cost a fortune.]]></content:encoded>
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        <item>
            <title><![CDATA[PyTorch DataLoader Storage Benchmarks: Throughput That Actually Matters]]></title>
            <link>https://localhost:10000/blog/pytorch-dataloader-storage-benchmarks</link>
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            <pubDate>Wed, 20 May 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[Synthetic storage benchmarks lie about what DataLoader performance feels like in practice. Here's how to measure what your training pipeline actually cares about.]]></content:encoded>
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        <item>
            <title><![CDATA[POSIX Filesystems on Object Storage: The Good, the Bad, the Fast]]></title>
            <link>https://localhost:10000/blog/posix-filesystems-object-storage</link>
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            <pubDate>Sat, 16 May 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[POSIX semantics on top of object storage is an old and messy problem. Here's what's possible, what's impossible, and what ML teams should actually demand from a storage layer.]]></content:encoded>
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        <item>
            <title><![CDATA[How Regional Caching Gateways Cut ML Data Loading Time by 10x]]></title>
            <link>https://localhost:10000/blog/regional-caching-gateways-ml-training</link>
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            <pubDate>Tue, 12 May 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[A caching gateway colocated with your GPUs is the biggest single lever for training throughput. Here's how the architecture works and why it produces such dramatic speedups.]]></content:encoded>
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        <item>
            <title><![CDATA[AWS EFS vs Training Pipes: A Cost Breakdown for ML Workloads]]></title>
            <link>https://localhost:10000/blog/efs-vs-training-pipes-ml-cost</link>
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            <pubDate>Mon, 04 May 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[EFS gives you POSIX on AWS. Training Pipes gives you POSIX plus caching plus any cloud. We ran the numbers on a realistic ML training workload to see which wins.]]></content:encoded>
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            <title><![CDATA[A Practical Guide to Mounting Cloud Storage for GPU Training]]></title>
            <link>https://localhost:10000/blog/mounting-cloud-storage-for-gpu-training</link>
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            <pubDate>Sun, 26 Apr 2026 00:00:00 GMT</pubDate>
            <content:encoded><![CDATA[Step-by-step guide to mounting cloud storage as a filesystem on your GPU nodes, without the usual FUSE pain or EFS sticker shock.]]></content:encoded>
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